• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.56.3-56
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• 2001

This study describes the jump error smoothing scheme with fuzzy logic based on the scalar adaptive filter. The scalar adaptive filter is an useful algorithm for smoothing abrupt jump errors. However, the performances of scalar adaptive algorithm depend on the variance of real signal. So to design an effective algorithm, many informations of real and jump signal are required. In this paper, the fuzzy rules are designed by the analysis of scalar adaptive filter, and then the improved and simplified scheme is developed for smoothing the jump error. Simulations to INS/GPS integrated system show that the proposed method is effective.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1989.10a
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• pp.64-75
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• 1989

This paper addresses the "PC vs. mainframe" issue by systematically evaluating the usefulness of PCs in an educational context. For this purpose the satisfaction of 47 undergraduate students working with a software package that is available on both PCs and the mainframe was measured and analyzed. The results of the analysis of variance show no interaction effects between computing context and computing experience. Users were more satisfied with PC LINDO than with its mainframe counterpart. Also experienced users showed significantly higher satisfaction than inexperienced users in this study.his study.

• Journal of Korean Society for Quality Management
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• v.15 no.1
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• pp.63-67
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• 1987

Basic problems in the study of detecting outliers from data of experimental designs are that they are difficult to detect and their presence influences the analysis of variance of the data set. This article is concerned with mainly detecting outliers in two-way tables with no replications. Various methods are reviewed and their relations to the Andrews-Pregibon's Statistic and Cook's Statistic are derived.

• Journal of Environmental Science International
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• v.17 no.4
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• pp.405-412
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• 2008

Statistical analysis technique was applied to operating parameters and removal efficiency data sets obtained from advanced wastewater treatment plant during 1 year. Through factor analysis three factors derived varimax rotation were selected each plant. Three components explained 96%, 87% of the total variance of the process, respectively. The components on $A_2O$ Plant were identified in the following order : 1) Shortening the SRT during high-flow period, 2) Keeping biomass high on winter 3) factor was related to DO. On DNR plant, we defined them as follows: factor 1, Prolonged the SRT during high-flow period; factor 2 was related to sludge return; factor 3, Influent BOD during low-DO period. This technique was believed to assist operators in identifying priorities to improve operation efficiency.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.557-573
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• 2015

In this paper we develop a Bayesian inference for a multiplicative double seasonal autoregressive (DSAR) model by implementing a fast, easy and accurate Gibbs sampling algorithm. We apply the Gibbs sampling to approximate empirically the marginal posterior distributions after showing that the conditional posterior distribution of the model parameters and the variance are multivariate normal and inverse gamma, respectively. The proposed Bayesian methodology is illustrated using simulated examples and real-world time series data.

• Communications for Statistical Applications and Methods
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• v.3 no.3
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• pp.283-290
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• 1996

The simulation study shows that the rank transform test has relatively superior power advantages over the parametric analysis of variance test in many cases for a $2^3$ factorial design, particularly with heavy-tailed distributions of the error terms. However the rank transform test should be cautiously used when all main effects and interactions related to a testing effect are possibly present at the same time.

• Journal of Gifted/Talented Education
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• v.5 no.2
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• pp.121-138
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• 1995

A group of 84 young children were administered the Preschool embedded Figures Test(PEFT). A three way analysis of variance was performed utilizing giftedness (gifted, normal), gender, and year(4 year-old class, 5 year-old class) as independent variables. The giftedness main effect was statistically significant. Gifted children were more effect not year main effect were statistically significant. The interaction effects between giftedness and gender, between giftedness and year, between gender and year were not significant either.

• Korean Journal of Remote Sensing
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• v.6 no.2
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• pp.153-164
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• 1990

A statistical correlation between cloud thickness and brightness is shown by regression analysis using the least-square method. Cloud thicknesses are obtained from radiosonde observation. Brightness values are obtained from GMS visible channel. Regression analyses are preformed on both thickness data used in conjunction with brightness data for summer season. The results are shown by the regression curve relating thickness and brightness accounting for 79% of variance. And the relationship between thickness and precipitable water in the cloud layers is analyzed. The thickness shows a positive correlation with precipitable water in cloudy layers.

• Communications for Statistical Applications and Methods
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• v.20 no.3
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• pp.225-233
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• 2013

The linear support vector machine(SVM) is motivated by the maximal margin separating hyperplane and is a popular tool for binary classification tasks. Many studies exist on the consistency properties of SVM; however, it is unknown whether the linear SVM is consistent for estimating the optimal classification boundary even in the simple case of two Gaussian classes with a common covariance, where the optimal classification boundary is linear. In this paper we show that the linear SVM can be inconsistent in the univariate Gaussian classification problem with a common variance, even when the best tuning parameter is used.

• Plant Biotechnology Reports
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• v.5 no.2
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• pp.135-146
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• 2011

The objective of the present research was to map QTLs associated with agronomic traits such as days from sowing to flowering, plant height, yield and leaf-related traits in a population of recombinant inbred lines (RILs) of sunflower (Helianthus annuus). Two field experiments were conducted with well-irrigated and partially irrigated conditions in randomized complete block design with three replications. A map with 304 AFLP and 191 SSR markers with a mean density of 1 marker per 3.7 cM was used to identify QTLs related to the studied traits. The difference among RILs was significant for all studied traits in both conditions. Three to seven QTLs were found for each studied trait in both conditions. The percentage of phenotypic variance ($R^2$) explained by QTLs ranged from 4 to 49%. Three to six QTLs were found for each yield-related trait in both conditions. The most important QTL for grain yield per plant on linkage group 13 (GYP-P-13-1) under partial-irrigated condition controls 49% of phenotypic variance ($R^2$). The most important QTL for 1,000-grain weight (TGW-P-11-1) was identified on linkage group 11. Favorable alleles for this QTL come from RHA266. The major QTL for days from sowing to flowering (DSF-P-14-1) were observed on linkage group 14 and explained 38% of the phenotypic variance. The positive alleles for this QTL come from RHA266. The major QTL for HD (HD-P-13-1) was also identified on linkage group 13 and explained 37% of the phenotypic variance. Both parents (PAC2 and RHA266) contributed to QTLs controlling leaf-related traits in both conditions. Common QTL for leaf area at flowering (LAF-P-12-1, LAF-W-12-1) was detected in linkage group 12. The results emphasise the importance of the role of linkage groups 2, 10 and 13 for studied traits. Genomic regions on the linkage groups 9 and 12 are specific for QTLs of leaf-related traits in sunflower.